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  <div class="headertitle"><div class="title">WeightedSumKernel.h</div></div>
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<a href="_weighted_sum_kernel_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Weighted sum of m_base kernels.</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      T.Glasmachers, O. Krause, M. Tuma</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2010, 2011</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * </span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#ifndef SHARK_MODELS_KERNELS_WEIGHTED_SUM_KERNEL_H</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#define SHARK_MODELS_KERNELS_WEIGHTED_SUM_KERNEL_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_kernel_function_8h.html" title="abstract super class of all kernel functions">shark/Models/Kernels/AbstractKernelFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span> </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;boost/utility/enable_if.hpp&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment"></span> </div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// \brief Weighted sum of kernel functions</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">///</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// For a set of positive definite kernels \f$ k_1, \dots, k_n \f$</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// with positive coeffitients \f$ w_1, \dots, w_n \f$ the sum</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// \f[ \tilde k(x_1, x_2) := \sum_{i=1}^{n} w_i \cdot k_i(x_1, x_2) \f]</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// is again a positive definite kernel function.</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// Internally, the weights are represented as</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// \f$ w_i = \exp(\xi_i) \f$</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// to allow for unconstrained optimization.</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">///</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// This variant of the weighted sum kernel eleminates one redundant</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// degree of freedom by fixing the first kernel weight to 1.0.</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">///</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// The result of the kernel evaluation is devided by the sum of the</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// kernel weights, so that in total, this amounts to fixing the sum</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">/// of the of the weights to one.</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">///</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">/// \ingroup kernels</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType=RealVector&gt;</div>
<div class="foldopen" id="foldopen00064" data-start="{" data-end="};">
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html">   64</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_weighted_sum_kernel.html" title="Weighted sum of kernel functions.">WeightedSumKernel</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction</a>&lt;InputType&gt;</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>{</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">base_type</a>;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span> </div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    <span class="keyword">struct </span>InternalState: <span class="keyword">public</span> <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>{</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        RealMatrix result;</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        std::vector&lt;RealMatrix&gt; kernelResults;</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        std::vector&lt;boost::shared_ptr&lt;State&gt; &gt; kernelStates;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span> </div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>        InternalState(std::size_t numSubKernels)</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        :kernelResults(numSubKernels),kernelStates(numSubKernels){}</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span> </div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <span class="keywordtype">void</span> resize(std::size_t sizeX1, std::size_t sizeX2){</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>            result.resize(sizeX1, sizeX2);</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != kernelResults.size(); ++i){</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>                kernelResults[i].resize(sizeX1, sizeX2);</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>            }</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        }</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>    };</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#afe31030a33669b789e1d92d56da07882">   85</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#adbf700c2ece7236c70cef4b88777a733" title="batch input type of the kernel">base_type::BatchInputType</a> <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#afe31030a33669b789e1d92d56da07882">BatchInputType</a>;</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a6caa8268bc333f331d50366fc679419d">   86</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#a40e365cb5ec7d2776105a4aef4e78df3" title="Const references to InputType.">base_type::ConstInputReference</a> <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6caa8268bc333f331d50366fc679419d">ConstInputReference</a>;</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">   87</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_kernel_function.html#af923f26f3d015156bb5ac159b302311b" title="Const references to BatchInputType.">base_type::ConstBatchInputReference</a> <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a>;</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span> </div>
<div class="foldopen" id="foldopen00089" data-start="{" data-end="}">
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#aee096c1b04abdeb6cc2bdd5a5b01ea17">   89</a></span>    <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#aee096c1b04abdeb6cc2bdd5a5b01ea17">WeightedSumKernel</a>(std::vector&lt;<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a>* &gt; <span class="keyword">const</span>&amp; base){</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>( base.size() &gt; 0, <span class="stringliteral">&quot;[WeightedSumKernel::WeightedSumKernel] There should be at least one sub-kernel.&quot;</span>);</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span> </div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.resize( base.size() );</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aca8bc10711b10be934175ca051a9b64b" title="total number of parameters">m_numParameters</a> = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size()-1;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a763f50399f37666fc1f81a6fed05e062" title="whether the weights should be adapted">m_adaptWeights</a> = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        <span class="keywordflow">for</span> (std::size_t i=0; i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size() ; i++) {</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>            <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>( base[i] != NULL );</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel = base[i];</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight = 1.0;</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].adaptive = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        }</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a> = (double)<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size();</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span> </div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        <span class="comment">// set m_base class features</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        <span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#ac0c799ac75db64200256ed50d34d2411">hasFirstParameterDerivative</a> = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        <span class="keywordflow">for</span> ( <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i&lt;<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++ ){</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>            <span class="keywordflow">if</span> ( !<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;hasFirstParameterDerivative() ) {</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>                <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#ac0c799ac75db64200256ed50d34d2411">hasFirstParameterDerivative</a> = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>                <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            }</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        }</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        <span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a505ca00275044073f08aae949127a76f">hasFirstInputDerivative</a> = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        <span class="keywordflow">for</span> ( <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i&lt;<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++ ){</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>            <span class="keywordflow">if</span> ( !<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;hasFirstInputDerivative() ) {</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#a505ca00275044073f08aae949127a76f">hasFirstInputDerivative</a> = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>                <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>            }</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        }</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span> </div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        <span class="keywordflow">if</span> ( <a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#ac0c799ac75db64200256ed50d34d2411">hasFirstParameterDerivative</a> )</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>            this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdead621a9ae065d91a154055a38a7ea72f8" title="is the kernel differentiable w.r.t. its parameters?">base_type::HAS_FIRST_PARAMETER_DERIVATIVE</a>;</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span> </div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        <span class="keywordflow">if</span> ( hasFirstInputDerivative )</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            this-&gt;<a class="code hl_variable" href="classshark_1_1_abstract_kernel_function.html#aa13e9ab3b8bbad9e1d773468671703e6">m_features</a>|=<a class="code hl_enumvalue" href="classshark_1_1_abstract_kernel_function.html#af54c80ca837961761506e6c2eec15bdeae4bd575af084f862f64bc665cad4c4ec" title="is the kernel differentiable w.r.t. its inputs?">base_type::HAS_FIRST_INPUT_DERIVATIVE</a>;</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>    }</div>
</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment"></span> </div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00127" data-start="{" data-end="}">
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#abe44dc95e08b7024712f4980e6dbd310">  127</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#abe44dc95e08b7024712f4980e6dbd310" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;WeightedSumKernel&quot;</span>; }</div>
</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment"></span> </div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// Check whether m_base kernel index is adaptive.</span></div>
<div class="foldopen" id="foldopen00131" data-start="{" data-end="}">
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a86efd4f545c8abf4caea4d9c38589e80">  131</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a86efd4f545c8abf4caea4d9c38589e80" title="Check whether m_base kernel index is adaptive.">isAdaptive</a>( std::size_t index )<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[index].adaptive;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span><span class="comment">    /// Set adaptivity of m_base kernel index.</span></div>
<div class="foldopen" id="foldopen00135" data-start="{" data-end="}">
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#aa433177f587bf2a79c7ec36977f15f00">  135</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#aa433177f587bf2a79c7ec36977f15f00" title="Set adaptivity of m_base kernel index.">setAdaptive</a>( std::size_t index, <span class="keywordtype">bool</span> b = <span class="keyword">true</span> ) {</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[index].adaptive = b;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a3ea97fa30195ad2fda455a58fa2e2839">updateNumberOfParameters</a>();</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span><span class="comment">    /// Set adaptivity of all m_base kernels.</span></div>
<div class="foldopen" id="foldopen00140" data-start="{" data-end="}">
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a227f996baf7f509c9cfe2e95f0ba1135">  140</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a227f996baf7f509c9cfe2e95f0ba1135" title="Set adaptivity of all m_base kernels.">setAdaptiveAll</a>( <span class="keywordtype">bool</span> b = <span class="keyword">true</span> ) {</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        <span class="keywordflow">for</span> (std::size_t i=0; i!= <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++)</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].adaptive = b;</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a3ea97fa30195ad2fda455a58fa2e2839">updateNumberOfParameters</a>();</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>    }</div>
</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="comment"></span> </div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="comment">    /// Get the weight of a kernel</span></div>
<div class="foldopen" id="foldopen00147" data-start="{" data-end="}">
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a3b684ed2ebcb1c8502e8116ee1ba8153">  147</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a3b684ed2ebcb1c8502e8116ee1ba8153" title="Get the weight of a kernel.">weight</a>(std::size_t index){</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(index &lt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size());</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[index].weight;</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    }</div>
</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>    </div>
<div class="foldopen" id="foldopen00152" data-start="{" data-end="}">
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a74cdcb5818e16690ee1088f6d5c2e77e">  152</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a74cdcb5818e16690ee1088f6d5c2e77e">setAdaptiveWeights</a>(<span class="keywordtype">bool</span> b){</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a763f50399f37666fc1f81a6fed05e062" title="whether the weights should be adapted">m_adaptWeights</a> = b;</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span><span class="comment"></span> </div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment">    /// return the parameter vector. The first N-1 entries are the (log-encoded) kernel</span></div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span><span class="comment">    /// weights, the sub-kernel&#39;s parameters are stacked behind each other after that.</span></div>
<div class="foldopen" id="foldopen00158" data-start="{" data-end="}">
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a7947a32a41b0bff7ac5e1f5532cccf51">  158</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a7947a32a41b0bff7ac5e1f5532cccf51">parameterVector</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        std::size_t num = <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a0d8ada3a0f91d423094039784f700461" title="Return the number of parameters.">numberOfParameters</a>();</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        RealVector ret(num);</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span> </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        std::size_t index = 0;</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        <span class="keywordflow">for</span> (; index != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size()-1; index++){</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>            ret(index) = std::log(<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[index+1].<a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a3b684ed2ebcb1c8502e8116ee1ba8153" title="Get the weight of a kernel.">weight</a>);</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span> </div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        }</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <span class="keywordflow">for</span> (std::size_t i=0; i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++){</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>            <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].adaptive){</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>                std::size_t n = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;numberOfParameters();</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>                subrange(ret,index,index+n) = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;parameterVector();</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>                index += n;</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>            }</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        }</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordflow">return</span> ret;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>    }</div>
</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment"></span> </div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">    ///\brief creates the internal state of the kernel</span></div>
<div class="foldopen" id="foldopen00178" data-start="{" data-end="}">
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a8f5c7a811096153b2cc2add881ea3b41">  178</a></span><span class="comment"></span>    boost::shared_ptr&lt;State&gt; <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a8f5c7a811096153b2cc2add881ea3b41" title="creates the internal state of the kernel">createState</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        InternalState* state = <span class="keyword">new</span> InternalState(<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size());</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); ++i){</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>            state-&gt;kernelStates[i]=<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;createState();</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        }</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <span class="keywordflow">return</span> boost::shared_ptr&lt;State&gt;(state);</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>    }</div>
</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="comment"></span> </div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="comment">    /// set the parameter vector. The first N-1 entries are the (log-encoded) kernel</span></div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span><span class="comment">    /// weights, the sub-kernel&#39;s parameters are stacked behind each other after that.</span></div>
<div class="foldopen" id="foldopen00188" data-start="{" data-end="}">
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a3682a26ae5a4261c1be65d8d672d9252">  188</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a3682a26ae5a4261c1be65d8d672d9252">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters) {</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(newParameters.size() == <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a0d8ada3a0f91d423094039784f700461" title="Return the number of parameters.">numberOfParameters</a>());</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span> </div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a> = 1.0;</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        std::size_t index = 0;</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>        <span class="keywordflow">for</span> (; index != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size()-1; index++){</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>            <span class="keywordtype">double</span> w = newParameters(index);</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[index+1].weight = std::exp(w);</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a> += <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[index+1].weight;</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span> </div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        }</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        <span class="keywordflow">for</span> (std::size_t i=0; i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++){</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>            <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].adaptive){</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>                std::size_t n = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;numberOfParameters();</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>                <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;setParameterVector(subrange(newParameters,index,index+n));</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>                index += n;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>            }</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>        }</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    }</div>
</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span> </div>
<div class="foldopen" id="foldopen00208" data-start="{" data-end="}">
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a0d8ada3a0f91d423094039784f700461">  208</a></span>    std::size_t <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a0d8ada3a0f91d423094039784f700461" title="Return the number of parameters.">numberOfParameters</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aca8bc10711b10be934175ca051a9b64b" title="total number of parameters">m_numParameters</a>;</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>    }</div>
</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span><span class="comment"></span> </div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span><span class="comment">    /// Evaluate the weighted sum kernel (according to the following equation:)</span></div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span><span class="comment">    /// \f$ k(x, y) = \frac{\sum_i \exp(w_i) k_i(x, y)}{sum_i exp(w_i)} \f$</span></div>
<div class="foldopen" id="foldopen00214" data-start="{" data-end="}">
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a41a6851266bc6808f2e271e764989349">  214</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a41a6851266bc6808f2e271e764989349">eval</a>(<a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6caa8268bc333f331d50366fc679419d">ConstInputReference</a> x1, <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6caa8268bc333f331d50366fc679419d">ConstInputReference</a> x2)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        <span class="keywordtype">double</span> numerator = 0.0;</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        <span class="keywordflow">for</span> (std::size_t i=0; i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++){</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>            <span class="keywordtype">double</span> result = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;eval(x1, x2);</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>            numerator += <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight*result;</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        }</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>        <span class="keywordflow">return</span> numerator / <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>    }</div>
</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span><span class="comment"></span> </div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span><span class="comment">    /// Evaluate the kernel according to the equation:</span></div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span><span class="comment">    /// \f$ k(x, y) = \frac{\sum_i \exp(w_i) k_i(x, y)}{sum_i exp(w_i)} \f$</span></div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span><span class="comment">    /// for two batches of inputs.</span></div>
<div class="foldopen" id="foldopen00226" data-start="{" data-end="}">
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a2b481f814c6863916b53c9c8918e8bec">  226</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a2b481f814c6863916b53c9c8918e8bec">eval</a>(<a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX1, <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX2, RealMatrix&amp; result)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>        std::size_t sizeX1 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX1);</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>        std::size_t sizeX2 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX2);</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>        ensure_size(result,sizeX1,sizeX2);</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>        result.clear();</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span> </div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>        RealMatrix kernelResult(sizeX1,sizeX2);</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>        <span class="keywordflow">for</span> (std::size_t i = 0; i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++){</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;eval(batchX1, batchX2,kernelResult);</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>            result += <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight*kernelResult;</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>        }</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>        result /= <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    }</div>
</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span><span class="comment"></span> </div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span><span class="comment">    /// Evaluate the kernel according to the equation:</span></div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span><span class="comment">    /// \f$ k(x, y) = \frac{\sum_i \exp(w_i) k_i(x, y)}{sum_i exp(w_i)} \f$</span></div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span><span class="comment">    /// for two batches of inputs.</span></div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span><span class="comment">    /// (see the documentation of numberOfIntermediateValues for the workings of the intermediates)</span></div>
<div class="foldopen" id="foldopen00244" data-start="{" data-end="}">
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#ad58a7538e994a5d0a0b5d49e7d582f87">  244</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#ad58a7538e994a5d0a0b5d49e7d582f87">eval</a>(<a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX1, <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX2, RealMatrix&amp; result, <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a>&amp; state)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>        std::size_t sizeX1 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX1);</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        std::size_t sizeX2 = <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX2);</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        ensure_size(result,sizeX1,sizeX2);</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>        result.clear();</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span> </div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        InternalState&amp; s = state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        s.resize(sizeX1,sizeX2);</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span> </div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        <span class="keywordflow">for</span> (std::size_t i=0; i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++){</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;eval(batchX1,batchX2,s.kernelResults[i],*s.kernelStates[i]);</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>            result += <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight*s.kernelResults[i];</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        }</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        <span class="comment">//store summed result</span></div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        s.result=result;</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>        result /= <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>    }</div>
</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span> </div>
<div class="foldopen" id="foldopen00262" data-start="{" data-end="}">
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a32e469b9516edfadd503609e68c2ab4a">  262</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a32e469b9516edfadd503609e68c2ab4a">weightedParameterDerivative</a>(</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX1,</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX2,</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        RealMatrix <span class="keyword">const</span>&amp; coefficients,</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>        RealVector&amp; gradient</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>    )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        ensure_size(gradient,<a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a0d8ada3a0f91d423094039784f700461" title="Return the number of parameters.">numberOfParameters</a>());</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span> </div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>        std::size_t numKernels = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); <span class="comment">//how far the loop goes;</span></div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span> </div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        InternalState <span class="keyword">const</span>&amp; s = state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span> </div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>        <span class="keywordtype">double</span> sumSquared = <a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>); <span class="comment">//denominator</span></div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span> </div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>        <span class="comment">//first the derivative with respect to the (log-encoded) weight parameter</span></div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>        <span class="comment">//the first weight is not a parameter and does not need a gradient.</span></div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>        <span class="comment">//[Theoretically, we wouldn&#39;t need to store its result .]</span></div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>        <span class="comment">//calculate the weighted sum over all results</span></div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>        <span class="keywordtype">double</span> numeratorSum = sum(coefficients * s.result);</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span>        <span class="keywordflow">for</span> (std::size_t i = 1; i != numKernels &amp;&amp; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a763f50399f37666fc1f81a6fed05e062" title="whether the weights should be adapted">m_adaptWeights</a>; i++) {</div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>            <span class="comment">//calculate the weighted sum over all results of this kernel</span></div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>            <span class="keywordtype">double</span> summedK=sum(coefficients * s.kernelResults[i]);</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>            gradient(i-1) = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight * (summedK * <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a> - numeratorSum) / sumSquared;</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>        }</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span> </div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>        std::size_t gradPos = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a763f50399f37666fc1f81a6fed05e062" title="whether the weights should be adapted">m_adaptWeights</a> ? numKernels-1: 0; <span class="comment">//starting position of subkerel gradient</span></div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>        RealVector kernelGrad;</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>        <span class="keywordflow">for</span> (std::size_t i=0; i != numKernels; i++) {</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>            <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a86efd4f545c8abf4caea4d9c38589e80" title="Check whether m_base kernel index is adaptive.">isAdaptive</a>(i)){</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>                <span class="keywordtype">double</span> coeff = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight / <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>                <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;weightedParameterDerivative(batchX1,batchX2,coefficients,*s.kernelStates[i],kernelGrad);</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>                std::size_t n = kernelGrad.size();</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>                noalias(subrange(gradient,gradPos,gradPos+n)) = coeff * kernelGrad;</div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>                gradPos += n;</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span>            }</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>        }</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>    }</div>
</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span><span class="comment"></span> </div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span><span class="comment">    /// Input derivative, calculated according to the equation:</span></div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span><span class="comment">    /// &lt;br/&gt;</span></div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span><span class="comment">    /// \f$ \frac{\partial k(x, y)}{\partial x}</span></div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span><span class="comment">    ///     \frac{\sum_i \exp(w_i) \frac{\partial k_i(x, y)}{\partial x}}</span></div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span><span class="comment">    ///          {\sum_i exp(w_i)} \f$</span></div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span><span class="comment">    /// and summed up over all  of the second batch</span></div>
<div class="foldopen" id="foldopen00307" data-start="{" data-end="}">
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#ac85e0509d78aeb0909c9a4b3876eb94f">  307</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#ac85e0509d78aeb0909c9a4b3876eb94f">weightedInputDerivative</a>(</div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX1,</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX2,</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>        RealMatrix <span class="keyword">const</span>&amp; coefficientsX2,</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#afe31030a33669b789e1d92d56da07882">BatchInputType</a>&amp; gradient</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>    )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(coefficientsX2.size1() == <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX1));</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(coefficientsX2.size2() == <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>(batchX2));</div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>        weightedInputDerivativeImpl&lt;BatchInputType&gt;(batchX1,batchX2,coefficientsX2,state,gradient);</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>    }</div>
</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span> </div>
<div class="foldopen" id="foldopen00319" data-start="{" data-end="}">
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a03eb586f4658acdf41643b761f932b3d">  319</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a03eb586f4658acdf41643b761f932b3d" title="From ISerializable, reads a metric from an archive.">read</a>(<a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a>&amp; ar){</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>        <span class="keywordflow">for</span>(std::size_t i = 0;i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); ++i ){</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>            ar &gt;&gt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight;</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>            ar &gt;&gt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].adaptive;</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>            ar &gt;&gt; *(<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel);</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>        }</div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>        ar &gt;&gt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>        ar &gt;&gt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aca8bc10711b10be934175ca051a9b64b" title="total number of parameters">m_numParameters</a>;</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span>    }</div>
</div>
<div class="foldopen" id="foldopen00328" data-start="{" data-end="}">
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a8e7dc5ce57c2378159ff080e49a18382">  328</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a8e7dc5ce57c2378159ff080e49a18382" title="From ISerializable, writes a metric to an archive.">write</a>(<a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a>&amp; ar)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>        <span class="keywordflow">for</span>(std::size_t i=0;i!= <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size();++i){</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>            ar &lt;&lt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight;</div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>            ar &lt;&lt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].adaptive;</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>            ar &lt;&lt; const_cast&lt;AbstractKernelFunction&lt;InputType&gt; <span class="keyword">const</span>&amp;&gt;(*(<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel));</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span>        }</div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span>        ar &lt;&lt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>        ar &lt;&lt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aca8bc10711b10be934175ca051a9b64b" title="total number of parameters">m_numParameters</a>;</div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span>    }</div>
</div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span> </div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span><span class="keyword">protected</span>:<span class="comment"></span></div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span><span class="comment">    /// structure describing a single m_base kernel</span></div>
<div class="foldopen" id="foldopen00340" data-start="{" data-end="};">
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno"><a class="line" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html">  340</a></span><span class="comment"></span>    <span class="keyword">struct </span><a class="code hl_struct" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html" title="structure describing a single m_base kernel">tBase</a></div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>    {</div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno"><a class="line" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html#a98d3de94e66213b695c95be5f80f3468">  342</a></span>        <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a>* <a class="code hl_variable" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html#a98d3de94e66213b695c95be5f80f3468" title="pointer to the m_base kernel object">kernel</a>;  <span class="comment">///&lt; pointer to the m_base kernel object</span></div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno"><a class="line" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html#a97d62b38ae459d7cc1eca3533de0cea7">  343</a></span>        <span class="keywordtype">double</span> <a class="code hl_variable" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html#a97d62b38ae459d7cc1eca3533de0cea7" title="weight in the linear combination">weight</a>;                              <span class="comment">///&lt; weight in the linear combination</span></div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"><a class="line" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html#ad3110b2d0681b54e7e053ff8fc20f67d">  344</a></span>        <span class="keywordtype">bool</span> <a class="code hl_variable" href="structshark_1_1_weighted_sum_kernel_1_1t_base.html#ad3110b2d0681b54e7e053ff8fc20f67d" title="whether the parameters of the kernel are part of the WeightedSumKernel&#39;s parameter vector?">adaptive</a>;                              <span class="comment">///&lt; whether the parameters of the kernel are part of the WeightedSumKernel&#39;s parameter vector?</span></div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span>    };</div>
</div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span> </div>
<div class="foldopen" id="foldopen00347" data-start="{" data-end="}">
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a3ea97fa30195ad2fda455a58fa2e2839">  347</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a3ea97fa30195ad2fda455a58fa2e2839">updateNumberOfParameters</a>(){</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aca8bc10711b10be934175ca051a9b64b" title="total number of parameters">m_numParameters</a> = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a763f50399f37666fc1f81a6fed05e062" title="whether the weights should be adapted">m_adaptWeights</a>? <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size()-1 : 0;</div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>        <span class="keywordflow">for</span> (std::size_t i=0; i != <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); i++)</div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>            <span class="keywordflow">if</span> (<a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].adaptive)</div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span>                <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aca8bc10711b10be934175ca051a9b64b" title="total number of parameters">m_numParameters</a> += <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;numberOfParameters();</div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>    }</div>
</div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span> </div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span>    <span class="comment">//we need to choose the correct implementation at compile time to ensure, that in the case, InputType</span></div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span>    <span class="comment">//does not implement the needed operations, the implementation is replaced by a safe default which throws an exception</span></div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span>    <span class="comment">//for this, we use enable_if/disable_if. The method is called with BatchInputType as template argument.</span></div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span>    <span class="comment">//real implementation first.</span></div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span>    <span class="keyword">template</span> &lt;<span class="keyword">class</span> T&gt;</div>
<div class="foldopen" id="foldopen00359" data-start="{" data-end="}">
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a8b3fc6653bb38681f0292d81251103a4">  359</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a8b3fc6653bb38681f0292d81251103a4">weightedInputDerivativeImpl</a>(</div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX1,</div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX2,</div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span>        RealMatrix <span class="keyword">const</span>&amp; coefficientsX2,</div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#afe31030a33669b789e1d92d56da07882">BatchInputType</a>&amp; gradient,</div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span>        <span class="keyword">typename</span> boost::enable_if&lt;boost::is_same&lt;T,RealMatrix &gt; &gt;::type* dummy = 0</div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno">  366</span>    )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span>        std::size_t numKernels = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>.size(); <span class="comment">//how far the loop goes;</span></div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span>        InternalState <span class="keyword">const</span>&amp; s = state.<a class="code hl_function" href="structshark_1_1_state.html#a9847e65e063245c6b02371c8b84f8da3" title="Safely downcast State to it&#39;s derived type.">toState</a>&lt;InternalState&gt;();</div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span> </div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span> </div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>        <span class="comment">//initialize gradient with the first kernel</span></div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span>        <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[0].kernel-&gt;weightedInputDerivative(batchX1, batchX2, coefficientsX2, *s.kernelStates[0], gradient);</div>
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno">  373</span>        gradient *= <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[0].weight / <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;</div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#afe31030a33669b789e1d92d56da07882">BatchInputType</a> kernelGrad;</div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>        <span class="keywordflow">for</span> (std::size_t i=1; i != numKernels; i++){</div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span>            <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].kernel-&gt;weightedInputDerivative(batchX1, batchX2, coefficientsX2, *s.kernelStates[i], kernelGrad);</div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span>            <span class="keywordtype">double</span> coeff = <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>[i].weight / <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;</div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span>            gradient += coeff * kernelGrad;</div>
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno">  379</span>        }</div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>    }</div>
</div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span>    <span class="keyword">template</span> &lt;<span class="keyword">class</span> T&gt;</div>
<div class="foldopen" id="foldopen00382" data-start="{" data-end="}">
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a9c33d52aa95a25710f74bfbcd32c1e8b">  382</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_weighted_sum_kernel.html#a9c33d52aa95a25710f74bfbcd32c1e8b">weightedInputDerivativeImpl</a>(</div>
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno">  383</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX1,</div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#a6c0691276ad97eaf6030cb2e5ab24679">ConstBatchInputReference</a> batchX2,</div>
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno">  385</span>        RealMatrix <span class="keyword">const</span>&amp; coefficientsX2,</div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span>        <a class="code hl_struct" href="structshark_1_1_state.html" title="Represents the State of an Object.">State</a> <span class="keyword">const</span>&amp; state,</div>
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno">  387</span>        <a class="code hl_typedef" href="classshark_1_1_weighted_sum_kernel.html#afe31030a33669b789e1d92d56da07882">BatchInputType</a>&amp; gradient,</div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span>        <span class="keyword">typename</span> boost::disable_if&lt;boost::is_same&lt;T,RealMatrix &gt; &gt;::type* dummy = 0</div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span>    )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno">  390</span>        <span class="keywordflow">throw</span> <a class="code hl_define" href="_exception_8h.html#a4e03d7dfdfe8cbc90447fa829fc09e4f">SHARKEXCEPTION</a>(<span class="stringliteral">&quot;[WeightedSumKernel::weightdInputDerivative] The used BatchInputType is no Vector&quot;</span>);</div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span>    }</div>
</div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span> </div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529">  393</a></span>    std::vector&lt;tBase&gt; <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a4028738f9aa22e1edab8867bb2aff529" title="collection of m_base kernels">m_base</a>;                      <span class="comment">///&lt; collection of m_base kernels</span></div>
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b">  394</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aff1ee3807f90b696b4e5e367d0091e1b" title="sum of all weights">m_weightsum</a>;                             <span class="comment">///&lt; sum of all weights</span></div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#aca8bc10711b10be934175ca051a9b64b">  395</a></span>    std::size_t <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#aca8bc10711b10be934175ca051a9b64b" title="total number of parameters">m_numParameters</a>;                   <span class="comment">///&lt; total number of parameters</span></div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno"><a class="line" href="classshark_1_1_weighted_sum_kernel.html#a763f50399f37666fc1f81a6fed05e062">  396</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_weighted_sum_kernel.html#a763f50399f37666fc1f81a6fed05e062" title="whether the weights should be adapted">m_adaptWeights</a>;                           <span class="comment">///&lt; whether the weights should be adapted</span></div>
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno">  397</span>};</div>
</div>
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno">  398</span> </div>
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno"><a class="line" href="namespaceshark.html#a20ac8f44e864d15b5c49ab113954b1b2">  399</a></span><span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_weighted_sum_kernel.html" title="Weighted sum of kernel functions.">WeightedSumKernel&lt;&gt;</a> <a class="code hl_typedef" href="namespaceshark.html#a20ac8f44e864d15b5c49ab113954b1b2">DenseWeightedSumKernel</a>;</div>
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"><a class="line" href="namespaceshark.html#aa7a6815d06144becc2cb687ea6b36a89">  400</a></span><span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_weighted_sum_kernel.html" title="Weighted sum of kernel functions.">WeightedSumKernel&lt;CompressedRealVector&gt;</a> <a class="code hl_typedef" href="namespaceshark.html#aa7a6815d06144becc2cb687ea6b36a89">CompressedWeightedSumKernel</a>;</div>
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno">  401</span> </div>
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno">  402</span>}</div>
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno">  403</span><span class="preprocessor">#endif</span></div>
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